影響消費者使用冷錢包簽帳卡之研究

、線上人數：166

、訪客IP：3.145.177.173

姓名	張正民(Cheng-Min Chang) 查詢紙本館藏	畢業系所	資訊管理學系在職專班
論文名稱	影響消費者使用冷錢包簽帳卡之研究 (Exploring consumer adoption of cold crypto-wallet debit cards)
檔案	[Endnote RIS 格式] [Bibtex 格式] [相關文章] [文章引用] [完整記錄] [館藏目錄] 至系統瀏覽論文 ( 永不開放)
摘要(中)	本研究以科技接受模式將內部變項之「認知有用性」、「認知易用性」、「使用態度」、「使用意圖」做為研究構面，並設計構面因子，以探討影響消費者使用冷錢包簽帳卡的各個面向。透過研究背景與研究動機，本研究目的如下： 1. 了解消費者使用冷錢包簽帳卡意願。 2. 了解消費者使用冷錢包簽帳卡認知有用性。 3. 了解消費者使用冷錢包簽帳卡認知易用性。 4. 了解消費者使用冷錢包簽帳卡在認知有用性與認知易用性下是否影響其使用態度與使用意圖。本研究透過Google表單問卷系統發送，總計回收有效問卷112份。研究結果顯示，冷錢包簽帳卡在科技接受模式的構面中，認知易用性對認知有用性、認知易用性對使用態度、認知有用對使用態度、認知有用性對使用意圖、使用態度對使用意圖，皆具有正面顯著的影響。以「冷錢包+簽帳卡」架構串接加密貨幣數位資產與法定貨幣下，冷錢包簽帳卡可將Web 3.0環境中的加密數位貨幣交割、轉換為法定貨幣，並經由BIN sponsor所提供的簽帳功能，作為在實體世界刷卡交易的工具，大幅度提升虛擬貨幣等數位資產持有者在現實生活中使用的便利程度，可說是嫁接數位資產與法定貨幣資產的最後一哩路。透過此研究可以了解影響消費者使用冷錢包簽帳卡有哪些面向可以著重，以利於未來的推廣發行。
摘要(英)	This study employs the Technology Acceptance Model (TAM), using the internal variables of "perceived usefulness," "perceived ease of use," "attitude towards use," and "behavioral intention to use" as research dimensions. These dimensions are designed as factors to explore the various aspects influencing consumers′ use of cold crypto-wallet debit cards. Through the research background and motivation, the objectives of this study are as follows: 1. To understand consumers′ willingness to use cold crypto-wallet debit cards. 2. To understand consumers′ perceived usefulness of cold crypto-wallet debit cards. 3. To understand consumers′ perceived ease of use of cold crypto-wallet debit cards. 4. To understand whether perceived usefulness and perceived ease of use affect consumers′ attitude towards use and behavioral intention to use cold crypto-wallet debit cards. The study collected data through the Google Forms survey system, yielding a total of 112 valid responses. The research results indicate that in the dimensions of the Technology Acceptance Model for cold crypto-wallet debit cards, perceived ease of use positively and significantly affects perceived usefulness, perceived ease of use positively and significantly affects attitude towards use, perceived usefulness positively and significantly affects attitude towards use, perceived usefulness positively and significantly affects behavioral intention to use, and attitude towards use positively and significantly affects behavioral intention to use. The "cold crypto-wallet + debit card" framework connects digital assets in cryptocurrencies with fiat currencies. Cold crypto-wallet debit cards can convert cryptocurrencies into fiat currencies within the Web 3.0 environment. With the debit function provided by the BIN sponsor, these cards can be used for transactions in the physical world, significantly enhancing the convenience for holders of digital assets such as virtual currencies in real life. This can be considered the final step in bridging digital assets and fiat assets. Through this research, we can understand what aspects influence consumers to use cold crypto-wallet debit cards that can be focused on to facilitate future promotion and issuance.
關鍵字(中)	★ 加密貨幣錢包 ★ 冷錢包 ★ 區塊鏈 ★ 冷錢包簽帳卡 ★ 冷錢包資安	關鍵字(英)	★ cryptocurrency wallet ★ cold crypto-wallet ★ blockchain ★ cold crypto-wallet debit card ★ cold crypto-wallet security
論文目次	摘要 i Abstract ii 誌謝 iii 目錄 iv 圖目錄 vii 表目錄 viii 一、緒論 1 1-1 研究背景與動機 1 1-1-1 研究背景 1 1-1-2 研究動機 4 1-2 研究目的 5 1-3 研究流程 6 二、文獻探討 9 2-1加密貨幣錢包與簽帳卡 9 2-1-1加密貨幣錢包發展 9 2-1-2簽帳卡發展 13 2-2加密貨幣錢包與簽帳卡資安問題 13 2-2-1加密貨幣錢包資安問題 13 2-2-2簽帳卡資安問題 15 2-3冷錢包簽帳卡金融現況 16 2-3-1冷錢包簽帳卡的交易 16 2-3-2冷錢包水庫來自加密貨幣交易所 16 2-3-3冷錢包簽帳卡的金流 17 2-4 科技接受模式定義與應用 18 2-4-1 科技接受模式定義 18 2-4-2 科技接受模型變數說明 20 2-4-3 TAM與加密貨幣研究應用進程 21 三、研究方法 24 3-1 研究架構 24 3-2 研究假說 24 3-3 測量工具 25 3-3-1研究樣本 25 3-3-2問卷設計 25 3-4 統計分析應用 27 3-4-1描述統計分析 27 3-4-2信度分析 27 3-4-3收斂效度分析 28 3-4-4 Bartlett′s球型檢定及KMO 28 3-4-5 Pearson 相關分析 29 3-4-6線性迴歸分析 29 四、結果與分析 30 4-1計數分析 30 4-1-1單選題 30 4-1-2複選題交叉分配表與卡方分配 32 4-2描述性統計 36 4-3信度分析 37 4-4因素收斂分析 38 4-4-1 KMO 與 Bartlett 檢定 38 4-4-2主成分分析 39 4-4-3以負荷量縮減尺度 39 4-5皮爾森相關分析 40 4-6迴歸分析 41 4-6-1認知易用性對認知有用性迴歸分析 41 4-6-2認知易用性對使用態度迴歸分析 43 4-6-3認知有用性對使用態度迴歸分析 44 4-6-4認知有性對使用意圖迴歸分析 45 4-6-5使用態度對使用意圖迴歸分析 46 五、結論 47 5-1 研究結果 47 5-2 研究貢獻 48 5-3管理意涵 48 5-4研究限制 49 5.5未來研究 50 參考文獻 51 附錄 55 一、冷錢包簽帳卡 TAM問卷調查選擇題 56 二、冷錢包簽帳卡 TAM問卷調查感知題項 57
參考文獻	[1] 羅鈺珊，<分散式帳本與區塊鏈的應用現況與挑戰>，經濟前瞻，173，2017，79-84頁。 [2] 經濟研究員王淳與李彧，<加密數字貨幣發展現狀及對金融市場的影響>，香港金融研究院香港經濟與政策處研究報告，2021年5月3日。 [3] Imvesting.com：2023年市值排名前10加密貨幣大盤點比特幣、以太幣仍佔據半壁江山，2023/12/13，取自https://hk.investing.com/news/stock-market-news/article-419703 [4] CoinMakerCap：2017-2024年BTC加密貨幣市值，取自https://coinmarketcap.com/zh-tw/charts/ [5] 每日幣研：7-11/全家買比特幣?如何在台灣便利商店買幣教學! 2023年5月17 日，取自https://cryptowesearch.com/blog/all/711-familymart-buy-crypto [6] Das, P., Faust, S., & Loss, J. (2019). A formal treatment of deterministic wallets. In Proceedings of the 2019 ACM SIGSAC conference on computer and communications security (pp. 651-668). [7] 財經新報：串接加密貨幣！台灣銘板與 MatchMove 首創 Web3.0 簽帳卡， 2024年2月1 日，取自https://finance.technews.tw/2024/02/01/matchmove/ [8] Khan, A. G., Zahid, A. H., Hussain, M., & Riaz, U. (2019, November). Security of cryptocurrency using hardware wallet and qr code. In 2019 International Conference on Innovative Computing (ICIC) (pp. 1-10). IEEE. [9] TUDUM：Bitconned Is a Cryptocurrency Cautionary Tale，2024年1月1 日，取自https://www.netflix.com/tudum/articles/bitconned-release-date-cast-news#:~:text=Bitconned%2C%20the%20new%20documentary%20from,a%20penchant%20for%20living%20large [10] Fareed, T. (2023). A Systemic Review of Payment Technologies with a Special Focus on Digital Wallets. Financial Technologies and DeFi: A Revisit to the Digital Finance Revolution, 89-97. [11] Davis, F., Bagozzi, R. & Warshaw, P. (1989). User acceptance of computer technology: a comparison of two theoretical models, Management Science, 35(8), 982-1003. [12] 維基百科：密碼貨幣錢包，2023年3月15日，取自https://zh.wikipedia.org/wiki/%E5%AF%86%E7%A0%81%E8%B4%A7%E5%B8%81%E9%92%B1%E5%8C%85 [13] Swan：Bitcoin Cold Storage: How to Protect Your Assets in 2024，2024年3月30日，取https://www.swanbitcoin.com/bitcoin-cold-storage/ [14] MoneyNote：2023十個最佳比特幣冷熱錢包排名及推薦，2023年2月3日，取自https://www.moneynotes.tw/best-crypto-wallets/ [15] Sam投資趨勢：台灣五大加密貨幣交易所推薦，一張圖看懂交易所比較分析，2024年3月14日，取自https://www.samchoulove.com/cryptocurrency-exchange-analysis/ [16] Shon, T. H., & Swatman, P. M. (1998). Identifying effectiveness criteria for Internet payment systems. Internet Research, 8(3), 202-218. [17] Mastercard：支付流程是如何運作，2024年，取自https://www.mastercard.com.hk/zh-hk/business/merchants/start-accepting/payment-process.html [18] Mirza, D., & Rahulamathavan, Y. (2023). Security Analysis of Android Hot Cryptocurrency Wallet Applications. In Data Protection in a Post-Pandemic Society: Laws, Regulations, Best Practices and Recent Solutions (pp. 79-111). Cham: Springer International Publishing. [19] Ebrahimi, S., Hasanizadeh, P., Aghamirmohammadali, S. M., & Akbari, A. (2021). Enhancing Cold Wallet Security with Native Multi-Signature schemes in Centralized Exchanges. Arxiv Preprint Arxiv:2110.00274. [20] Courtois, N. T., Emirdag, P., & Valsorda, F. (2014). Private key recovery combination attacks: On extreme fragility of popular bitcoin key management, wallet and cold storage solutions in presence of poor RNG events. Cryptology ePrint Archive. [21] Jasem, F. M., Sagheer, A. M., & Awad, A. M. (2021). Enhancement of digital signature algorithm in bitcoin wallet. Bulletin of Electrical Engineering and Informatics, 10(1), 449-457. [22] 鄭瑞忠與王明紅，<不可替代代幣－機制和市場的初步研究>，CCISA 通訊，28 (3)，2022，15-30頁。 [23] GC, A. (2013). Credit Card Security. Rovaniemen Ammattikorkeakoulu Lapin Mmattikorkeakoulu. https://urn.fi/URN:NBN:fi:amk-2013121321198 [24] Dara, J., & Gundemoni, L. (2006). Credit Card Security and E-Payment: Enquiry into credit card fraud in E-Payment. [25] Khalid, A. R., Owoh, N., Uthmani, O., Ashawa, M., Osamor, J., & Adejoh, J. (2024). Enhancing credit card fraud detection: an ensemble machine learning approach. Big Data and Cognitive Computing, 8(1), 6. [26] 冷錢包簽帳卡樣張，峮峮和台灣銘板聯名公益套卡，2023年，取自https://sports.tnp.cards [27] Fishbein, M., & Ajzen, I. (1977). Belief, attitude, intention, and behavior: An introduction to theory and research. Philosophy and Rhetoric, 10(2).130-132. [28] Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention and behavior: an introduction to theory and research. Reading, MA: Addition-Wesley [29] 吳智鴻與蔡依錞，<以科技接受模式來探討社群網站Facebook的使用意圖>。Using Technology Acceptance Model for Investigating the Social Network Website (Facebook) Usage Intenton]，10(1)，2014，29-44頁。 [30] Sagheer, N., Khan, K. I., Fahd, S., Mahmood, S., Rashid, T., & Jamil, H. (2022). Factors affecting adaptability of cryptocurrency: An application of technology acceptance model. Frontiers in psychology, 13, 903473. [31] Arias-Oliva, M., Pelegrín-Borondo, J., & Matías-Clavero, G. (2019). Variables influencing cryptocurrency use: a technology acceptance model in Spain. Frontiers in psychology, 10, 438810. [32] Koksalmis, G. H., Arpacı, İ., & Koksalmis, E. (2022). Predicting the intention to use bitcoin: an extension of technology acceptance model (TAM) with perceived risk theory. In Recent Innovations in Artificial Intelligence and Smart Applications (pp. 105-120). Cham: Springer International Publishing. [33] Jariyapan, P., Mattayaphutron, S., Gillani, S. N., & Shafique, O. (2022). Factors influencing the behavioural intention to use cryptocurrency in emerging economies during the COVID-19 pandemic: Based on technology acceptance model 3, perceived risk, and financial literacy. Frontiers in Psychology, 12, 814087. [34] Taheri Tolu, M., Yazdanian, N., Hemmati, H., & KORDLOUIE, H. (2023). Accepting Financial Transactions using Blockchain‌ Technology‌ and Cryptocurrency: A Customer Perspective Approach. Journal of Investment Knowledge, 12(46), 465-484. [35] Gupta, D., Garg, K., & Goel, V. (2024). Factors Influencing the Decision Behaviour of Gen-Z To Invest in Cryptocurrency: An Application of Utaut Model. Journal of Informatics Education and Research, 4(1). [36] Singh, S., Sahni, M. M., & Kovid, R. K. (2020). What drives FinTech adoption? A multi-method evaluation using an adapted technology acceptance model. Management Decision, 58(8), 1675-1697. [37] Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., ＆ Tatham, R. L. (2006). Multivariate data analysis, 6, Prentice hall Upper Saddle River, NJ. [38] Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50.
指導教授	蔡志豐周恩頤	審核日期	2024-7-23
推文	facebook plurk twitter funp google live udn HD myshare reddit netvibes friend youpush delicious baidu
網路書籤	Google bookmarks del.icio.us hemidemi myshare

博碩士論文 111453023 詳細資訊